Although multiple risk prediction models have been developed to identify malnutrition in dialysis patients, their quality and performance remain unclear, limiting their practicality in current clinical practice and future research. Therefore, we conducted a systematic review and meta-analysis to evaluate these models. Searches were conducted in PubMed, Embase, Web of Science, The Cochrane Library, CINAHL, SinoMed, CNKI, Wanfang, and VIP Database from inception to January 26, 2026. Two investigators independently screened the literature, extracted data, and assessed quality using the Prediction model Risk of Bias Assessment Tool (PROBAST). Meta-analyses of the prevalence of malnutrition, common predictors and model performance were performed using Stata 18.0 and R 4.5.1. A total of 12 eligible studies conducted in China were included, and the pooled prevalence of malnutrition in dialysis patients was 41%. Meta-analysis identified age, serum calcium, Kt/V, triglycerides, sex, vitamin D, NT-proBNP, and comorbid diabetes as statistically significant predictors. The pooled effect of the nine internal validated models was 0.83, indicating good discriminatory performance. However, all included models were rated at high risk of bias, primarily due to inappropriate data sources and poor reporting of the analysis. The current analysis reveals a high prevalence of malnutrition among dialysis patients. Eight significant predictors were identified, guiding future selection for constructing predictive models of malnutrition risk in this population. Although existing models demonstrate adequate discriminatory performance, their methodological limitations constrain clinical applicability. Future studies should prioritize the development of standardized, externally validated models to enable early identification and intervention, thereby improving outcomes in this vulnerable group.
Zinc pollution is a growing environmental concern because of its potential for bioaccumulation and associated toxicity. Microorganisms have attracted considerable interest in environmental governance because of their environmentally benign nature and high efficiency in removing heavy metals. In this study, Stenotrophomonas sepilia FCHC-ZnA2, which has strong Zn-chelating activity, was isolated from highly Zn-polluted soil in China. SEM and TEM analyses showed that S. sepilia FCHC-ZnA2 effectively absorbed Zn2+ through extracellular polymeric substances (EPS) on its surface and formed particles. FTIR and XPS analyses confirmed that -SH, C = O, and -OH groups within the EPS matrix effectively enriched free Zn2+ and facilitated the formation of insoluble deposits (ZnS) on the bacterial surface. 3D-EEM analysis revealed that Tyr and Trp provided adsorption sites during Zn2+ adsorption. Proteomic analysis revealed that under Zn2+ stress, S. sepilia FCHC-ZnA2 adapts by enhancing pyruvate metabolism and fatty acid degradation to supply sulfur-containing amino acids while upregulating ribosomal proteins to support EPS synthesis. Subsequently, the downregulation of the MetNIQ transporter suggests a cellular strategy to restrict external sulfur uptake, thereby promoting ZnS nucleation and precipitation.
The restructuring of aging Chinese city infrastructure requires new approaches based on computational intelligence and optimization lifecycle structures. Current building renovation methods are limited by the lack of seamless linkage between real-time operation data and predictive lifecycle management, particularly regarding privacy and multi-building management in heterogeneous environments. This paper introduces a simulation framework that combines federated deep reinforcement learning and behavioural digital twins to restore Chinese buildings into smart cities. The architecture involves a three-layer system: a physical layer with IoT-enabled sensing networks in distributed building clusters, a digital twin layer with real-time BIM-to-operational model synchronization and LiDAR-enhanced geometric precision, and an intelligent layer with privacy-reflecting federated proximal policy optimization for distributed decision-making. The framework addresses the gap between fixed 3D representations and variable behavioural modelling by integrating continuous learning processes that respond to changing occupancy, energy consumption, and structural decay. Simulation studies of Chinese urban residential communities show better performance: 27.3% lower lifecycle operational costs, 34.6% improved energy efficiency with the same thermal comfort, and 39.7% better structural integrity prediction with CFRP-optimized improvements. The federated learning architecture results in 5.8% cost savings and 6.2% emission reduction, offering scalable, privacy-sensitive urban renewal decision support for China's modernization efforts.
In recent years, new cases of thyroid cancer (TC) in China have accounted for about 10% of all newly diagnosed malignant tumors, ranking as the third most common cancer. Familial papillary thyroid carcinoma (fPTC) is a hereditary subtype for which large-scale clinical cohort studies are lacking and definitive susceptibility genes remain elusive. A large fPTC clinical cohort (171 cases), 490 sporadic papillary thyroid carcinoma (sPTC) patients, and 500 healthy blood samples from physical examination were collected in the study. Whole-genome sequencing (WGS) and whole-exome sequencing (WES) were used to screen for susceptibility genes. Three MCM2 gene mutations (c.1092 C > G, p.N364K; c.1975A>G, p.I659V; and c. 2379 G > A, p.M793I) in 8 patients from 4 distinct families were identified as candidate susceptibility variants. These mutations disrupt the interaction of MCM2 with its partner proteins (MCM3-7), leading to ubiquitination of free MCM monomers. Levels of DNA damage, γ-H2AX foci, RPA foci, and micronucleus formation were significantly elevated in MCM2-deficient cells. Cell-derived xenograft (CDX) modeling, combined with WES and RNA-seq analyses, revealed that MCM2-deficient tumors exhibited significantly faster growth rates and increased chromosomal instability (CIN). MAPK signaling and the PI3K-AKT pathway were significantly over-activated in MCM2-deficient tumors. In our study, based on the fPTC cohort, germline variants of MCM2 predispose to fPTC. The variants disrupt the MCM complex, leading to ubiquitination of free monomeric MCM proteins. MCM2 deficiency induces cell cycle arrest, DNA damage, and CIN, ultimately accelerating tumorigenesis through oncogenic pathway activation. These findings identify MCM2 as a low-frequency, moderately penetrant susceptibility gene for fPTC and underscore the clinical value of MCM2 testing in informing early detection, preventive management, and precision treatment strategies for familial papillary thyroid carcinoma.
Sleep disorders are prevalent and constitute a major concern in patients with diabetes mellitus. Therefore, the aim of this study was to investigate the applicability of machine learning methods in predicting sleep disorders among diabetic patients. Six relevant features were selected using single-factor correlation analysis and the LASSO algorithm. We developed and evaluated five ML models: logistic regression, decision tree, extreme gradient boosting, support vector machine, and light gradient boosting machine. Data from the China Health and Retirement Longitudinal Study database were utilized, with a total of 60,308 elderly individuals screened, of which 1276 diabetic patients were included in the analysis. Of these, 777 did not develop sleep disorders, while 499 did. Fifteen statistically significant predictors were identified through single-factor analysis, and six relevant variables were determined via LASSO regression, including family history of diabetes, education, marital status, chronic diseases, chronic pain, and depression. Based on these six variables, five ML models were constructed to predict the risk of sleep disorders in diabetic patients. Among these, the XGB model demonstrated superior performance, with an area under the curve of 0.850. The calibration curve indicated a good fit of the model on the development set, and decision curve analysis further confirmed the model's excellent net benefit and prediction accuracy. The overall performance of the XGB model was the best. Our findings suggest that ML models, particularly extreme gradient boosting, offer the most effective approach for predicting the risk of sleep disorders in diabetic patients.
Radiotherapy (RT) efficacy is limited by RT-induced immune resistance. Here we show that RT upregulates programmed death ligand 1 (PD-L1) on senescent tumor cells (STCs) via bromodomain-containing protein 4 (BRD4) signaling, thereby promoting immune evasion. To counter this, we develop POLY-Senolytic, a polymeric senolytic nanoparticle formed by conjugating an acid-responsive polymer to a peptide-based BRD4 PROteolysis-TArgeting Chimera via a reduction-cleavable disulfide bond. The POLY-Senolytic is activated in the acidic and reductive intracellular environment of tumor cells, leading to BRD4 degradation, suppression of RT-induced PD-L1 expression and enhanced immune clearance of STCs. Combined with RT, the POLY-Senolytic suppresses tumor growth and metastasis in orthotopic mouse models of pancreatic and breast tumors. We further engineer a β-galactosidase-responsive POLY-Tracker for real-time monitoring of senolytic therapy. Together, this study identifies an RT-driven BRD4-PD-L1 axis in STCs that promotes immune resistance and provides a practical strategy to eliminate and track them.
We evaluated whether ovarian tissue cryopreservation (OTC) is a viable option for girls and young women who have already undergone hematopoietic stem cell transplantation (HSCT). We enrolled 142 patients who underwent OTC either before or after HSCT. Participants were categorized into three groups: "after HSCT group" (n = 16), "no-chemo group" before OTC (n = 75), and "chemo group" before OTC (n = 51). The after HSCT cohort had a mean age of 12.06 years at OTC, with half being pre-pubertal. Comparative analysis across age (0-12 and 13-24 years) revealed that the after-HSCT group consistently presented with significantly elevated FSH and diminished AMH levels relative to the no-chemo group. Ovarian volume per ovary was also markedly reduced in the after-HSCT group compared to the no-chemo group across all ages. Crucially, the estimated total follicle count per ovary was significantly lower in the after-HSCT group than in both pre-HSCT groups among 0-12 years, and versus the "no-chemo group" in 13-24 years. This confirms pre-HSCT fertility preservation as the paramount strategy. However, OTC post-HSCT emerges as a viable salvage option for carefully selected patients, underscoring the need for long-term follow-up to identify candidates and expand fertility possibilities in survivorship care.
Chronic rhinosinusitis with nasal polyps (CRSwNP) frequently induces the formation of intracellular stress granules (SGs). This study aims to identify key genes linked to both CRSwNP and SGs, offering novel perspectives for improving CRSwNP treatment and management. Data from GSE136825, GSE179265, and SG-related gene sets were retrieved from public databases. Key genes were identified via machine learning, ROC analysis, and expression validation. Subsequently, nomogram construction, Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and drug prediction were performed to explore the regulatory mechanisms of these genes. Three candidate genes-ANG, CRYAB, and FBP1-passed ROC and expression validation and were ultimately identified as key players. GSEA revealed that these genes collectively participate in the cell cycle pathway. Immune infiltration analysis showed 23 differentially abundant immune cell types between the CRSwNP and control groups, with ANG, CRYAB, and FBP1 all exhibiting significant positive correlations with Type 2 T helper cells. Furthermore, estradiol, copper sulfate, and trichostatin A were identified as drugs interacting with all three key genes. These findings provide valuable insights for advancing CRSwNP diagnosis.
Esophageal squamous cell carcinoma (ESCC) has limited treatment options post-immune checkpoint inhibitor (ICI) resistance. We developed IPM514, a universal multi-epitope mRNA lipid nanoparticle (LNP) vaccine targeting tumor-associated antigens identified via transcriptomic analysis of TCGA/GTEx datasets and validated in 132 ESCC patients. IPM514 contains 15 fragments from 9 antigens, covering most patients and over half of HLA subtypes. Peripheral blood mononuclear cells (PBMCs) from healthy donors and ESCC patients stimulated with IPM514 effectively expanded specific T cells that exhibited significant cytotoxic activity against ESCC and other squamous cell carcinomas with similar antigen profiles. In HLA-transgenic mouse models, IPM514 suppressed tumor growth, extended survival, and provided durable protection. Importantly, combination therapy with PD-1 blockade augmented antitumor efficacy by promoting immune cell infiltration, upregulating antigen presentation pathways, and reprogramming the tumor microenvironment toward an anti-tumorigenic state. These results demonstrate IPM514 represents a promising novel mRNA vaccine strategy for improving ESCC immunotherapy.
High-pressure gas intrusion during drilling operations poses significant well control challenges, heightening blowout risks. To address this critical safety concern, this paper proposes a real-time monitoring framework by characterizing gas-liquid flow pattern transitions through ultrasonic Doppler detection and time-frequency analysis. The proposed methodology systematically integrates three innovative processes: (1) gas-phase channel enumeration for spatial quantification of gas distribution patterns, (2) Doppler frequency shift range analysis enabling dynamic flow characteristic resolution, and (3) flow pattern threshold calibration to establish robust classification criteria. The thresholds of various flow patterns are obtained by the number of gas-phase channels and Doppler frequency shift range to achieve the purpose of real-time monitoring of gas intrusion status. To validate the methodology, an experimental gas intrusion simulation system was developed, comprising a wellbore pipeline integrated with a drilling fluid circulation pool and an ultrasound Doppler detection array. This system replicates downhole flow pattern transitions by injecting controlled gas velocities (0.1-5 m/s) at multiple axial positions, enabling spatial-temporal analysis of gas-liquid phase redistribution under simulated drilling conditions. The results show that, our method effectively explain the dynamic evolution of two-phase flow under different flow regimes and achieved early detection of phase transitions. This method provide a non-invasive ultrasound-based pipeline, offering a high-responsiveness solution for gas kick monitoring in high-risk drilling.
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Intelligent manufacturing demands accurate prediction of machining quality characteristics with conflicting behaviour, which is challenging with limited experimental data. Taguchi L27 experimental data sets were collected with 6 influencing variables (workpiece material: PA66, PA66 + GF30, PA66 + MoS₂, tool approach angle, tool nose radius, cutting speed, feed rate and depth of cut) and 8 machining quality characteristics (surface roughness, cutting force, temperature, amplitude of vibration, tool wear rate, specific cutting energy, material removal rate, and sound pressure level). The total 27 experimental datasets were stratified by material into a 3-fold cross-validation protocol. The present work develops five base learners such as Gaussian Process Regression (GPR), Least Squares Boosting (LSB), Support Vector Regression (SVR), Random Forest (RF) and extreme gradient boosting (XGBoost) for predictions of machining quality characteristics. All three metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and crayfish optimization algorithm (COA)) determine identical weights for three best predictive base learners (GPR, SVR, and LSB) for developing Ensemble model. The COA converge to a minimum composite cost with comparatively lesser computation time than GA and PSO. Therefore, CrayStack ensemble model is constructed with a hybrid combination of GPR, SVR, and LSB and COA methods. The COA efficiently optimizes the adaptive fusion weights by assigning a higher weight fraction to GPR and LSB for nonlinear models. CrayStack Ensemble predictions outperforms all individual learners (SVR, GPR, LSB, XGBoost, and RF) with material stratified three-fold cross validation across all eight outputs of training data. CrayStack Ensemble requires a total training cost of 16.13 s (which includes base model training: 94.92% & 15.31 s, COA weight optimization: 1.88% & 0.30 s, and bootstrap confidence interval estimation: 3.20% & 0.52 s) ensuring its practical usefulness. CrayStack Ensemble achieves near-instantaneous inference (0.003 ms/sample; 290,592 samples/s) with a speed of 58.33, 79.33, 4899.33, 5630.67, 5608.3 over GPR, SVR, LSB, RF and XGBoost ensuring practicality suitable for real-time monitoring systems. Wilcoxon single-rank test confirmed that improvements are statistically significant (with a preset confidence level, p < 0.05) for 7 of the 8 responses, validating the practical utility of the developed models. CrayStack Ensemble showed superior prediction performances against nine randomly generated test cases with a mean absolute percent error of 9.8%, followed by GPR, LSB, XGBoost, SVR, and RF of 12.69%, 13.81%, 15.39%, 21.52% and 42.73% considering all responses. The results demonstrated that the intelligent ensemble stack ensures robustness and higher prediction accuracy for limited experimental datasets offering a practical solution for industrial process optimization.
The hemoglobin, albumin, lymphocyte, and platelet (HALP) score has been reported to be associated with the progression and prognosis of various malignancies. However, its prognostic significance in patients with lung cancer remains controversial. We conducted a systematic evaluation to investigate the prognostic value of the HALP score in relation to survival outcomes in patients with lung cancer. A comprehensive search of PubMed, Embase, Web of Science, and the Cochrane Library was performed up to October, 2025, to identify studies assessing the association between HALP and lung cancer prognosis. Pooled hazard ratios (HRs) with corresponding 95% confidence intervals (CIs) were calculated for overall survival (OS), progression-free survival (PFS), recurrence-free survival (RFS), and disease-free survival (DFS). A total of 17 studies involving 10,635 lung cancer patients were included. Specifically, 15 studies (10,238 patients) assessed OS, 6 studies (1173 patients) assessed PFS, and 3 studies (362 patients) assessed DFS/RFS. The pooled analysis revealed that a low HALP score was significantly associated with poorer OS (HR 1.82, 95%CI 1.46-2.28, p < 0.001), PFS (HR 1.66, 95%CI 1.17-2.35, p < 0.001), and DFS/RFS (HR 2.78, 95%CI 1.14-6.78, p < 0.001). Subgroup analyses further confirmed that pretreatment HALP was an independent predictor of OS in lung cancer patients. A lower pretreatment HALP score was significantly associated with poorer prognosis in patients with lung cancer. Although HALP may represent a promising and easily accessible prognostic biomarker, its clinical application is currently constrained by substantial cut-off heterogeneity and the lack of externally validated thresholds. Future large-scale prospective studies should establish standardized cut-off values and evaluate HALP as part of integrated prognostic models.
The geometry of the Circle of Willis poses major challenges for mechanical thrombectomy, where device navigability and effective thrombus removal determine treatment success. This study investigated the performance of venturi-inspired aspiration thrombectomy devices in a simplified cerebral artery segment representative of the middle cerebral artery (MCA), a frequent site of occlusion. Five designs (30°, 45°, 60° venturi, 7/11° taper, and cylindrical control) were assessed using a combined computational-experimental framework. On the computational side, unsteady Reynolds-averaged Navier-Stokes (URANS) simulations were performed in ANSYS Fluent 19.2 with k-ε turbulence closure. Blood-clot interactions were modeled using a Volume of Fluid (VOF) multiphase formulation with Carreau-Yasuda non-Newtonian rheology. In vitro, stereolithography-fabricated prototypes were tested with porcine thrombi in silicone arterial phantoms. CFD predicted extraction times of 2.12 s for the control and 1.64 s for the 45° venturi, with efficiency plateauing beyond 45°. Experimental results confirmed this trend, showing the 45° design as optimal and all venturi devices outperforming the control. Fragmentation analysis revealed a trade-off, with the 60° venturi producing more than twice the fragments of the 30°. These findings demonstrate that venturi taper geometry critically influences aspiration efficiency and fragmentation and establish CFD-experiment integration as a foundation for optimizing next-generation thrombectomy devices.
Population transfer via chirped rapid adiabatic passage is studied using open quantum and semiclassical models, with and without the rotating-wave approximation. A time-dependent variational approach based on the multiple-Davydov D2 trial state is employed to simulate the quantum models with an arbitrary finite mean photon number. We examine the accuracy of both the semiclassical field description and the rotating-wave approximation. Robust population transfer is identified over a wide parameter regime controlled by the laser spectral chirp and is found to be insensitive to the spin-phonon coupling strength, Gaussian pulse area, and energy gap of the two-level system.
The progression of lung adenocarcinoma (LUAD) from pre-invasive lesions (atypical adenomatous hyperplasia/adenocarcinoma in situ) to invasive adenocarcinoma constitutes a complex, multi-dimensional, and dynamic process. While the genomic drivers underlying this transition have been extensively characterized, the mechanisms by which metabolic reprogramming remodels the tumor immune microenvironment to orchestrate immune escape remain a critical knowledge gap. Leveraging insights from multi-omics integration encompassing genomics, metabolomics, single-cell RNA sequencing, and spatial transcriptomics, this review systematically delineates the molecular mechanisms through which metabolic reprogramming acts as a pivotal driver of immune escape during LUAD evolution. We critically dissect the intricate metabolic-epigenetic-spatial-immune; specifically, we highlight the mechanism through which cancer cells impair immune effector function via nutrient deprivation and utilize oncometabolites to instigate epigenetic modifications, thereby imprinting an immunosuppressive phenotype at the genetic level. Furthermore, this review discusses the development of early-screening biomarkers based on metabolic signatures, as well as combination therapeutic strategies targeting dual metabolic-immune checkpoints. The aim is to provide a novel theoretical framework for improving clinical management and prognosis in patients with LUAD.
Bifunctional catalysis is pivotal to industrial heterogeneous catalytic processes, yet its performance is limited by the kinetic entanglement among different elementary steps. In the conversion of light alkanes to aromatics, a promising non-naphtha route to produce benzene, toluene and xylene (BTX) that examples this challenging, this paper describes a process-separated cascade catalysis (PSCC) for efficient BTX synthesis, prioritizing kinetic decoupling over spatial intimacy of active sites in bifunctional systems. We employ a spatially decoupled metal-zeolite catalyst, achieving >95% propane conversion and 82.3% aromatic selectivity with near-exclusive BTX formation at 550 °C during continuous reaction-regeneration cycles. In situ spectroscopies and kinetics analysis reveal that PSCC decouples kinetics of alkane dehydrogenation to olefine intermediates (ethene, propene) in the first stage and synchronizes this process with oligomerization and aromatization reactions in the second stage. This synchronization affords the rate matching of two stage, thus minimizes cracking by-products and enhances BTX production substantially. Combined with high catalytic stability and technoeconomic assessment, this PSCC strategy represents a robust pathway in alkane (C2-C4)-to-BTX conversion and beyond bifunctional catalysis processes.
Abnormal accumulation of Poly(ADP-ribose) polymerase 1 (PARP1) promotes cancer progression, yet its stabilization mechanisms remain unclear. Here, we identify E3 ubiquitin ligase tripartite motif-containing 21 (TRIM21) as a PARP1-binding partner. PARP1 interacts directly with TRIM21 via its 662-908 domain, while the PRY-SPRY domain of TRIM21 is essential for this binding. TRIM21 facilitates PARP1 polyubiquitination at residue K654, leading to its degradation. In small cell lung cancer (SCLC), TRIM21 is significantly downregulated, and its tumor-suppressive function is partly mediated through the degradation of PARP1, supporting genomic stability. Additionally, the PI3K/AKT pathway transcriptionally suppresses TRIM21 via transcription factor STAT5A, thereby stabilizing PARP1. Importantly, combining the PI3K/AKT inhibitor PKI-587 with the PARP inhibitor BMN673 synergistically inhibits tumor growth across multiple SCLC models, including cell lines, patient-derived organoids, and xenograft models. Collectively, our findings define a "PI3K/AKT-STAT5A-TRIM21-PARP1" axis critical for SCLC progression and propose its dual inhibition as a promising therapeutic strategy.
Octane numbers (ON) and derived cetane numbers (DCN) are widely used as indicators to quantify the ignition qualities of gasoline- and diesel-type fuels, respectively. In this study, a chemical kinetics-based methodology is proposed for quantitatively relating ignition delay times (IDTs) to the ON and subsequently deriving correlations to predict DCN. The methodology is based on the ignition-delay-time equivalence principle, according to which fuels exhibiting ignition delay times identical to those of primary reference fuel (PRF) mixtures under well-defined RON-like thermodynamic conditions are assigned the same octane number. To assess the predictive capability of this methodology, ignition delay times of both test fuels and PRF mixtures were simulated under RON-like conditions using detailed chemical kinetic mechanisms for toluene primary reference fuels (TPRF) developed independently by various research groups. The predicted ON values were validated against experimentally measured ON data obtained using standard ASTM methods. For PRF, TRF, and TPRF blends under RON-like conditions over 30-80 bar at φ = 1.0, the best overall correlation was achieved at 40 bar, with an R2 of 0.988 and an RMSE of 1.322. The validation results confirm that the proposed kinetic modeling approach provides reliable and accurate predictions of ON, with improved accuracy correlating closely with the precision of simulated ignition delay times. Subsequently, a linear correlation between the predicted ON and DCN values, measured in accordance with ASTM D6890 standards, was established. The predicted DCN values exhibit satisfactory agreement with experimental data under the specified RON-like conditions. The developed kinetic-based methodology provides valuable theoretical insights for engine design and operation, as well as the development of future fuels.
The physical properties of van der Waals materials are highly dependent on their stacking sequences. However, constructing layered materials with specific compositions and desired stackings is challenging. Here we show that a solvent-directed strategy enables targeted stacking in van der Waals metal-organic frameworks, an emerging class of van der Waals materials. By leveraging the tunable metastable states of conjugated ligands through various solvents, and stabilizing these states via metal-ligand coordination, we enable the inheritance of stacking patterns from ligands to van der Waals metal-organic frameworks. Notably, this strategy enables the controlled manipulation of stacking sequences and divergent charge transport regimes in two-dimensional and three-dimensional van der Waals metal-organic framework single crystals, yielding an electrical conductivity of 1,792 S cm-1. These results provide a versatile approach for designing layered materials with programmable stackings and tailored electronic properties.